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ORIGINAL ARTICLE

Spirometry and impulse oscillometry (IOS) for detection of respiratory abnormalities in metropolitan firefighters resp_1809

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TJARD SCHERMER,1,2 WINIFRED MALBON,1 WENDY NEWBURY,1 CHRISTINE HOLTON,1 MICHAEL SMITH,3 MICHAEL MORGAN3 AND ALAN CROCKETT1 1

Primary Care Respiratory Research Unit, Department of General Practice, School of Population Health & Clinical Practice, The University of Adelaide and 3South Australian Metropolitan Fire Service, Adelaide, South Australia, Australia, and 2Department of Primary and Community Care, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands

ABSTRACT

SUMMARY AT A GLANCE

Background and objective: As firefighters are at increased risk of adverse health effects, periodic examination of their respiratory health is important. The objective of this study was to establish whether the use of impulse oscillometry (IOS) reveals respiratory abnormalities in metropolitan firefighters that go undetected during routine respiratory health screening by spirometry and assessment of respiratory symptoms. Methods: This was a cross-sectional analysis of spirometry, IOS and questionnaire data from 488 male firefighters. Abnormal spirometry was defined as FEV1, FEV1/FVC and/or FEF50 below the lower limit of normal. Abnormal IOS was defined as resistance at 5 Hz (R5), frequency dependence of resistance (DR5-R20) and/or reactance area (AX) above the upper limit of normal. Respiratory symptoms, smoking history, exposures and medical history were assessed. Data were analysed using logistic and linear regression models. Results: The mean age of the firefighters was 43.8 (SD 8.4) years. There were 123 (25%) former smokers and 50 (10%) current smokers. Abnormal spirometry was detected in 12%, abnormal IOS in 9% and respiratory symptoms in 20% of firefighters. Current smoking was associated with all IOS parameters (OR for R5 = 3.1, OR for DR5-R20 = 7.7, OR for AX = 4.3), and with FEF50 (OR = 9.1), chronic productive cough (OR = 4.0) and breathlessness (OR = 5.4) (P < 0.05 for all). Exposure during firefighting duties was associated with chronic productive cough (OR = 2.6), but not with spirometry or IOS parameters. Interaction terms in the linear regression models indicated associations between smoking and DR5-R20, and also between smoking and

Firefighters are at increased risk of adverse health effects, thus periodic examination of their respiratory health is important. The use of impulse oscillometry for the assessment of respiratory health of firefighters identified airways dysfunction in some, even when respiratory symptoms were absent and spirometry values were within the normal range.

AX, in the lowest and second lowest quartiles of spirometry parameters. Conclusions: Application of IOS for the assessment of respiratory health in firefighters identified airways dysfunction in some individuals, even when spirometry values were within the normal range and there were no respiratory symptoms. Key words: lung injury, occupational health, oscillometry, respiratory mechanics, smoke inhalation.

INTRODUCTION

Correspondence: Tjard R. Schermer, Primary Care Respiratory Research Unit, Department of General Practice, School of Population Health & Clinical Practice, The University of Adelaide, Adelaide, SA 5005, Australia. Email: tjaarda.schermer@ adelaide.edu.au Received 16 February 2010; invited to revise 26 March 2010; revised 23 April 2010; accepted 27 April 2010 (Associate Editor: Chi Chiu Leung).

Despite the use of personal protection measures, firefighters are likely to be at increased risk of adverse health effects due to exposure to harmful substances. Retrospective cohort studies have shown an increased mortality among firefighters due to particular types of cancers, but a decreased lifetime risk of death due to chronic lung disease.1 Some, but not all, published studies that have investigated the respiratory health of firefighters have shown reduced lung function, accelerated lung function decline or increased airway responsiveness.2–4 Firefighters may be at increased risk of accelerated lung function decline depending on their actual exposures, but the published evidence is inconsistent.3,5–8 One study has demonstrated a correlation between the non-use of protective equipment and rate of lung function decline.9

© 2010 The Authors Journal compilation © 2010 Asian Pacific Society of Respirology

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976 Because of the potentially increased risk for the development of airways disease among firefighters, it is important that functional respiratory abnormalities be detected as early as possible. This permits appropriate measures to be taken to avoid further exposures, slow the progression of underlying abnormalities and thereby enable firefighters to continue in their physically demanding profession. Although spirometry is the conventional method for assessing abnormalities in lung function, it has been recognized to be an imperfect tool for identifying dysfunction of the peripheral airways.10 Studies on clinical populations and in occupational settings have demonstrated that oscillometry can be used to detect abnormal airways function in the presence of normal spirometry.11–18 Therefore, additional assessment of peripheral airways function when spirometry is normal would seem to be important in the evaluation of subjects exposed to occupational hazards. In a cohort study of South Australian metropolitan firefighters impulse oscillometry (IOS) was performed in parallel with spirometry. IOS is a non-invasive technique that allows assessment of respiratory mechanics independently of maximal cooperation from the subject,19 and that can be used to differentiate between peripheral and central airways disease.20 In this study we investigated whether IOS could detect respiratory abnormalities in male metropolitan firefighters that were not disclosed during routine screening with spirometry and assessment of respiratory symptoms.

METHODS Subjects and study design This was a cross-sectional study performed in a cohort of South Australian metropolitan firefighters. Recruitment commenced in August 2007 and data collection was completed in August 2008. At the time of the study, the South Australian Metropolitan Fire Service (SAMFS) maintained a staff of 955 firefighters, of whom 58% volunteered for the study. The SAMFS started to use respirators (positive pressure breathing apparatus) about 30 years ago, and all firefighters in the study had used respirators for their entire career. The majority of non-participants were firefighters who were stationed in rural or large country towns, at fire stations that were not or only occasionally visited by the research nurse. Thus, the main reason for nonparticipation was the unavailability of the research nurse during the firefighters’ shift, or that the firefighter was called out on service before or during the scheduled assessment. The study was approved by the University of Adelaide Human Research Ethics Committee and all participating firefighters gave written informed consent.

Spirometry and impulse oscillometry All assessments were performed at SAMFS stations by a single research nurse (W.M.). A MasterScreen Respirology (2010) 15, 975–985

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(Viasys Healthcare/Cardinal Health, Würzburg, Germany) with a Lily-type heated screen pneumotachograph was used for both spirometry and IOS. The research nurse was trained and extensively supervised by the principal investigator (A.C.) in the operation of the MasterScreen system and the performance of spirometry and IOS assessments using this system. Quality assurance consisted of a weekly review by the principal investigator of all tests conducted in the previous week with feedback to the research nurse. In order to avoid the effects of forced expirations on respiratory smooth muscle tone,21 IOS was performed prior to spirometry. Before each measurement, the equipment was calibrated using a standard 3-L syringe and a known, fixed resistance of 0.2 kPa/L/s to verify the pressure calibration and frequency fidelity. Spirometry included measurements of FEV1, FEV1/ FVC and FEF50. A minimum of three acceptable forced manoeuvres were performed.22 Small pressure oscillations of five pulses per second were generated by a loudspeaker attached to a mouthpiece. A minimum of three technically acceptable runs, each spanning 30 s of tidal breathing, were performed.19 During each of these 30 s runs, 97 repeated measurements were recorded at all frequencies between 5 and 35 Hz; the mean of these 97 measurements was taken as the result for each frequency. The decision as to which 30 s run was used was based on the absence of artefacts in the recording, a tidal volume between 0.5 and 1 L, and a coherence function (signal-to-noise ratio) >0.90 for all frequencies. Subjects were seated comfortably throughout, held their heads in a neutral position, wore nose clips, were instructed to keep their mouth tightly sealed around the mouthpiece, keep their tongue low in the mouth by tucking it under the mouthpiece, and to avoid swallowing or talking. The research nurse placed her hands firmly on the subject’s cheeks to minimize the shunt effect of the upper airways. Respiratory impedance (Z), resistance (R) and reactance (X) were calculated from the oscillatory components of flow and pressure at 5, 10, 15, 20, 25 and 35 Hz (referred to as R5, X5, etc.). IOS data also included measurements of resonance frequency (fres, frequency at which the inertial forces of the moving air column in the conducting airways and the elastic properties of the lung periphery are balanced), frequency dependence of airways resistance (fall in resistance from 5 to 20 Hz, DR5-R20) and heterogeneity of peripheral airways function (integrated low frequency reactance area or AX, calculated as the area under the curve from 5 Hz to fres).23

Anthropometric data and questionnaire Standing height without shoes was measured using wall-mounted stadiometers and weight in light clothing and without shoes was measured on a standard digital scale. A questionnaire was used to collect information on respiratory symptoms (productive cough, breathlessness and wheeze when exposed to trigger factors for ⱖ3 months each year), history of doctor-diagnosed respiratory disease (asthma, COPD, © 2010 The Authors Journal compilation © 2010 Asian Pacific Society of Respirology

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emphysema, chronic bronchitis), smoking status and history, and exposure to dust, smoke, vapours or fumes, and chemical products during firefighting and other professional activities. Because detailed information on occupational exposures was unavailable, self-reported average exposures during firefighting duties were categorized as 6 h/week. Regular exposure during other professional activities was dichotomized as being either present or absent.

Definitions of abnormal respiratory function Predicted normal equations for spirometry and IOS in healthy non-smoking South Australian men have been reported previously.24 This publication did not report the predicted normal equations for DR5-R20 and AX, which are shown in Appendix S1 (online supporting information). These equations were used in the present study to calculate the predicted normal values for the firefighters. Values were considered to be within normal limits if they were 1.645 SD above (upper limit of normal, ULN) the predicted mean value. Abnormal spirometry was defined as FEV1 and/or FEV1/FVC and/or FEF50 below LLN, and abnormal IOS was defined as R5, DR5-R20 and/or AX above ULN. The use of limits of normal to define abnormal spirometry and IOS is recommended in international guidelines.19,25 Because the use of IOS is still under development and there is currently no consensus about the indices that should be used to establish abnormal airways function, the decision to base the interpretation of IOS data on R5, DR5-R20 and AX was based on the opinions of IOS experts,20,23 and the use of these particular indices in recent studies.16,17 Firefighters were considered to be symptomatic with regard to respiratory symptoms if they reported chronic productive cough, breathlessness and/or chest tightness.

Statistical analyses Data were analysed using SPSS, version 17.0.0. As smoking is a well-known determinant of airways function as measured by spirometry,26 and smoking also affects IOS measurements,27 firefighters were stratified according to smoking status (never, former, current) before further analysis of the data. Analysis of variance and chi-square tests were used to compare characteristics between smoking status subgroups, and logistic regression was used to analyse associations between smoking, exposures and abnormal respiratory function. Non-parametric Spearman correlation coefficients were calculated to assess correlations between spirometry and IOS variables. Firefighters were categorized into quartiles based on the within-sample distributions of percentage predicted values for FEV1, FEV1/FVC and FEF50. In linear regression models, the IOS parameters indicating peripheral airways function were entered as dependent variables, with the respective lung function index quartiles, smoking status, exposures during © 2010 The Authors Journal compilation © 2010 Asian Pacific Society of Respirology

firefighting and other professional activities, doctordiagnosed respiratory diseases, age and BMI as covariates. Lung function index quartile by smoking status was included as an interaction term. Part of the analysis was repeated after exclusion of all firefighters who reported any respiratory symptom during the baseline assessment. For the comparison of subject characteristics among never, former and current smokers and for correlations between spirometry and IOS indices, statistical significance was defined as P < 0.05. To correct for multiple comparisons in the analysis of associations between spirometry and IOS indices, statistical significance was defined as P < 0.0125 (Bonferoni correction: a = 0.05/4, considering analysis of the associations among FEV1, FEV1/ FVC, DR5-R20 and AX, as being the most relevant).

RESULTS Study population The initial study population consisted of 552 firefighters. The 13 women were excluded and the population was further reduced to 501 male firefighters after exclusion of 38 temporary volunteers and new recruits, and to 488 after exclusion of those for whom data on smoking status were missing. Table 1 shows the details of the final study population, which consisted of 315 (65%) never smokers, 123 (25%) former smokers and 50 (10%) current smokers. Eighty-nine firefighters (18%) reported other professional activities during which they were exposed to potentially harmful factors (56 never smokers, 19 former smokers, 14 current smokers; P = 0.144). Doctor-diagnosed COPD, emphysema or chronic bronchitis was reported by 28 (6%), and doctordiagnosed asthma by 63 (13%) firefighters. More current smokers than former or never smokers reported chronic productive cough (P < 0.001) and breathlessness (P = 0.001). Average FEV1, FEV1/FVC and FEF50 values and percentage predicted values were lowest or tended to be lower in current smokers. Forced expiratory time was 11.0 s (SD 3.9) in never smokers, 11.8 s (4.4) in former smokers and 11.9 s (4.0) in current smokers (P = 0.11). DR5-R20 and AX values were significantly different between smoking status subgroups, and were highest in current smokers, although R5 was not. There were statistically significant differences between smoking status subgroups for mean IOS values of X5, X10, X15, X20 and X25, but not X35. There were no differences in resistance between the smoking status subgroups at any of the frequencies tested.

Smoking, exposures and respiratory function Comparing current smokers with never smokers, the ORs were >1 for abnormal FEF50, R5, DR5-R20, AX, chronic productive cough and breathlessness, but not for abnormal FEV1 or FEV1/FVC (Table 2). Among former smokers the ORs were not increased for any of Respirology (2010) 15, 975–985

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Table 1 Demographic details, respiratory symptoms, and spirometry and impulse oscillometry data by smoking status in 488 male firefighters Never smokers

Former smokers

Current smokers

488

315 (65)

123 (25)

50 (10)

43.8 (8.4) 1.80 (0.06) 89.5 (11.3) 27.6 (3.1)

43.2 (8.4) 1.80 (0.06) 88.9 (11.3) 27.3 (3.2)

45.6 (8.3) 1.79 (0.06) 90.7 (11.2) 28.2 (3.2)

42.9 (8.2) 1.80 (0.06) 90.8 (11.1) 27.9 (2.4)

0.019 0.187 0.227 0.016

41 (8) 36 (7) 47 (10) 28 (6) 63 (13)

19 (6) 17 (5) 35 (11) 14 (4) 39 (12)

9 (7) 9 (7) 8 (6) 8 (6) 16 (13)

13 (26) 10 (20) 4 (8) 6 (12) 8 (16)

ULN¶ DR5-R20, kPa/L/s n (%) > ULN¶ fres, Hz AX, kPa/L n (%) > ULN¶ Z5, kPa/L/s X5, kPa/L/s R10, kPa/L/s X10, kPa/L/s R15, kPa/L/s X15, kPa/L/s R20, kPa/L/s X20, kPa/L/s R25, kPa/L/s X25, kPa/L/s R35, kPa/L/s X35, kPa/L/s

P-value*

0.154 0.686 0.074 0.067 0.298 0.327

0.102 ULN

AX > ULN

1.09 (1.01, 1.16) 0.92 (0.77, 1.09)

1.44 (0.35, 6.02) 1.77 (0.30, 10.45)

1.17 (0.31, 4.36)

4.26 (1.24, 14.58) 1.52 (0.47, 4.88) 0.44 (0.05, 3.70)

Impulse oscillometry†

0.99 (0.95, 1.03) 0.98 (0.88, 1.09)

1.04 (1.00§§, 1.09) 1.04 (0.92, 1.16)

4.83 (2.12, 11.00) 0.93 (0.24, 3.56)

0.67 (0.23, 1.96)

5.41 (2.13, 13.74) 1.28 (0.53, 3.10) 0.92 (0.24, 3.53)

Breathlessness

Chest tightness‡

1.00 (0.96, 1.04) 0.98 (0.88, 1.10)

12.33 (5.95, 25.54) 3.83 (1.27, 11.51)

0.65 (0.24, 1.76)

0.45 (0.12, 1.67) 0.40 (0.16, 1.04) 0.81 (0.19, 3.46)

Respiratory symptoms

1.30 (0.55, 3.08) 5.95 (2.41, 14.65)

0.75 (0.33, 1.73)

3.98 (1.81, 8.74) 1.27 (0.59, 2.71) 2.64 (1.04, 6.65)

Productive cough

†

Data are ORs (95% CI). Statistically significant associations are indicated in bold. All LLNs and ULNs are based on the predicted normal values reported in Newbury et al.24 Pre-bronchodilator. ‡ On exposure to dust or other potential trigger factors. § Never smokers as reference. ¶ >6 h/week of exposure to dust, smoke and fire; ⱕ2 h/week of exposure as reference. †† Exposure to dust, smoke, vapours/fumes and/or chemical products. ‡‡ Doctor-confirmed. §§ Lower limit of 95% CI >1, but due to rounding off to two decimal places CI appears to include 1. —, No reliable estimate possible because number of subjects too small; AX, integrated low frequency reactance area; LLN, lower limit of normal; R, respiratory resistance; ULN, upper limit of normal.

Current smokers§ Former smokers§ High exposure during firefighting¶ Exposure during other professional activities†† Asthma‡‡ COPD, emphysema or bronchitis‡‡ Age BMI

FEV1 < LLN

Spirometry†

Respiratory outcomes

Table 2 Results of logistic regression analysis for the presence of abnormal respiratory outcomes in relation to possible determinants, in 488 male metropolitan firefighters


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980 the respiratory outcomes. Self-reported high exposures to smoke, dust and fire during firefighting duties were associated with chronic productive cough but not with breathlessness, chest tightness, or spirometry or IOS parameters. Doctor-diagnosed COPD, emphysema or chronic bronchitis was associated with productive cough, and doctor-diagnosed asthma was associated with breathlessness and chest tightness.

Spirometry and impulse oscillometry for detection of abnormal peripheral airways function Overall 12% of spirometry tests (58/488) and 9% of IOS tests (45/488) showed abnormal peripheral airways function (Fig. 1). The percentage of abnormal spirometry findings was highest among current smokers (20%) and lowest among never smokers (9%). Among the 430 firefighters with normal spirometry IOS indicated abnormality in 34 (8%), whereas among those with abnormal spirometry (n = 58) IOS indicated abnormality in 11 (19%). The addition of IOS to spirometry increased the probability of detecting peripheral airways dysfunction from 0.12 to 0.19. Correlations between spirometry and IOS parameters were moderate for FEV1 and low for FEV1/FVC and FEF50 (Table 3). Correlations among IOS parameters were moderate to high, with coefficients ranging from 0.548 to 0.822. In the majority of firefighters (39/58, 67%) abnormal spirometry was solely due to a FEV1/FVC below the LLN (Table 1). Among never smokers the mean FVC was higher in those with abnormal spirometry (6.04 L, 103.8% of predicted, n = 28) than in those with normal spirometry (5.90 L, 101.5% of predicted), whereas mean FEV1 was lower in those with normal spirometry (3.83 L, 84.8% of predicted vs 4.52 L, 100.1% of predicted) (Fig. 1). The two never smokers with abnormal spirometry, who also showed abnormal IOS, had similar FVC, FEV1 and FEV1/FVC values to the 26 never smokers with normal IOS. A similar pattern of high FVC values and relatively low FEV1 values in those with abnormal spirometry was observed among former smokers, but not current smokers. The four smokers with abnormal spirometry and abnormal IOS had much lower FVC and FEV1 values, but higher FEV1/FVC values compared with the six smokers with normal IOS. Among the former and current smokers, only those with abnormal IOS had consistently lower FEV1 and FVC values compared with those with normal IOS, irrespective of the spirometry measurements.

Associations between spirometry and impulse oscillometry The linear regression models consistently showed higher values for DR5-R20 and AX among current and former smokers. These two subgroups were therefore combined into one category for the final part of the Respirology (2010) 15, 975–985

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analysis. Figure 2a shows that the mean DR5-R20 value increased with quartiles of decreasing FEV1%, and this trend was clearly more distinct in current/ former smokers than in never smokers. The difference between current/former and never smokers was especially evident in the lowest quartile of FEV1%. The linear regression model showed that this trend was almost significant for the comparison between never smokers and current/former smokers (P = 0.038 for FEV1% quartile*smoking status interaction term). For AX this trend reached statistical significance (P = 0.009 for FEV1% quartile*smoking status interaction term). For FEV1/FVC% there was no trend in either DR5-R20 or AX among never smokers (Fig. 2b). However, the trends towards higher mean DR5-R20 values (P = 0.024) and AX values (P = 0.015) in the lowest two quartiles of FEV1/FVC% were close to significant among current/former smokers. Current/ former smokers in the lowest quartile of FEF50 % predicted had higher mean DR5-R20 and AX values compared with those in the highest three quartiles, and also compared with never smokers in all four quartiles (DR5-R20, P = 0.032; AX, P = 0.009). The linear regression analyses did not show statistically significant associations between R5 and FEV1, FEV1/ FVC or FEF50 (results not shown). When the analysis of DR5-R20 and AX was limited to asymptomatic subjects only, there was a trend towards higher mean DR5-R20 values among current/former smokers in the lowest two quartiles of FEV1/FVC (P = 0.035) (Fig. 3). A similar association of AX with FEV1/FVC quartiles among current/former smokers did not reach statistical significance (P = 0.088; results not shown).

DISCUSSION In this study we investigated whether or not use of IOS would reveal respiratory abnormalities in male metropolitan firefighters that would not be detected by routine spirometry screening and assessment of respiratory symptoms. Assessment of this large population of male firefighters showed that overall, 21% had abnormal spirometry and/or IOS. There were significant associations between current smoking and all IOS parameters, whereas for spirometry, only FEF50 was associated with current smoking. There was a moderate association between exposures during firefighting duties and productive cough, but not with spirometry or IOS parameters. There were statistically significant associations between smoking and DR5R20 and AX values in the lowest and second lowest quartiles of spirometry parameters, which illustrated the ability of IOS to detect peripheral airways dysfunction that would otherwise go unnoticed in firefighters with low spirometry values that are still within the normal range. Impulse oscillometry and spirometry have been previously compared in occupational settings. In a sample of iron workers, who were exposed after the World Trade Center (WTC) disaster, Skloot et al. showed that IOS R5 values were increased in 53% of subjects, whereas spirometry suggested airway © 2010 The Authors Journal compilation © 2010 Asian Pacific Society of Respirology


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Figure 1 Summary of normal and abnormal spirometry and impulse oscillometry findings by smoking status in 488 male firefighters. *Defined as FEV1, FEV1/FVC and/or FEF50 below the lower limit of normal.24 †Defined as R5, DR5-R20 and/or AX above the upper limit of normal.24 AX, integrated low frequency reactance area; IOS, impulse oscillometry; R, respiratory resistance.

obstruction in only 17%.16 Oppenheimer et al. observed increased R5, DR5-R20 and/or AX values in 68% of symptomatic subjects with normal spirometry, who were exposed to WTC dust.17 The particular nature of the WTC dust exposure and the lack of use of personal protection measures are plausible explanations for these much higher rates of abnormal IOS tests compared with the present study, in which abnormal spirometry was observed in 12% and abnormal IOS in 9% of metropolitan firefighters.

Airflow limitation and accelerated lung function decline have been reported in firefighters compared with non-firefighters, but the published evidence is inconsistent. Based on a longitudinal study of >12 000 New York City Fire Department rescue workers, Banauch et al. concluded that workers exposed to WTC dust experienced substantial declines in FEV1 and that this was correlated with the intensity of exposure.7 Sparrow et al. showed that there were greater declines in FVC and FEV1 among 168 US


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Table 3 Spearman correlation coefficients for correlations among spirometry and impulse oscillometry parameters in 488 male metropolitan firefighters

FEV1 FEV1/FVC FEF50 R5 DR5-20 AX

FEV1

FEV1/FVC

FEF50

R5

DR5-20

AX

1 0.379* 0.699* -0.385* -0.419* -0.452*

1 0.833* -0.202* -0.095* -0.083

1 -0.294* -0.236* -0.246*

1 0.632* 0.548*

1 0.822*

1

* Statistically significant at P < 0.05. AX, integrated low frequency reactance area; R, respiratory resistance.

firefighters than among 1474 control subjects,5 and Mustajbegovic et al. reported decreased FEF75 values among 128 Croatian firefighters compared with 88 control workers.28 In contrast, Horsfield et al. showed that the rate of lung function decline was lower among 96 UK firefighters compared with 69 nonsmoking control subjects.8 The inconsistency among these studies is probably due to differences in the selection of subjects, definition of outcomes, exposures and the use of respiratory protection devices. Clinical studies have shown that spirometric indices and respiratory symptoms are different domains of respiratory health and can therefore be expected to show low correlations.29 The present analyses suggest that this is also true for firefighters. Further follow up of this firefighter cohort will enable us to assess the declines in spirometry and IOS indices. One explanation for the observed association between relatively low FEV1 and relatively high FVC values in never-smoking firefighters is possible damage to the respiratory system caused by exposure to smoke, fumes and dust during firefighting, which may occur as a consequence of brief or inadequate use of personal protection measures. An alternative explanation may be the frequent and long-term use of self-contained breathing apparatus.30,31 Another explanation may involve the concept of airway/ parenchymal dysanapsis, a phenomenon in which individuals with large lungs have airways with diameters that are not correspondingly greater than those of persons with small lungs.32,33 The use of a ratio that combines a measure of airways diameter (FEV1) and a measure of lung volume (FVC) does not take into account this inter-individual independence of airway diameter and lung size.32 Finally, the relatively high FVC values may also be due to a healthy worker effect, whereby men who exercise during the lung growth phase tend to choose a professional career that requires exertion. Indeed, the SAMFS excludes recruits who are not sufficiently strong and fit from proceeding to a career as a firefighter. Although the major reason for non-participation in the study (42%) was logistic factors, some selection bias may have occurred in the recruitment of firefighters. Individual firefighters who suspected that their respiratory health may have been compromised (e.g. due to smoking habit), or who were aware of their limited physical fitness, and the possible conse-

quences these factors might have for their professional careers, may have been less likely to volunteer for the study. For both practical and financial reasons we were not able to perform more advanced lung function tests (e.g. plethysmography, oesophageal manometry) to compare spirometry and IOS results. However, other investigators have shown high correlations of DR5-R20 and AX values with lung compliance measurements in a sample of WTC rescue workers.17 In the present study detailed information on time spent in exposed settings, actual exposure and use of protective respiratory devices was unavailable. Furthermore, distinguishing between airways dysfunction caused by smoking, occupational exposures or other exposures was not possible. However, it is important to protect firefighters from further lung damage that may compromise their respiratory health or their ability to perform their professional duties, irrespective of whether the airways dysfunction is related to occupation (i.e. exposures), lifestyle (i.e. smoking) or other factors (e.g. an underlying respiratory condition such as asthma). In any case, early detection of respiratory dysfunction is important so that timely and appropriate measures can be taken. Other limitations of the study were our inability to validate the firefighters’ self-reported non-smoking status biochemically, and that it was not formally checked whether firefighters were experiencing a common cold or mild respiratory infection at the time of spirometry and IOS testing. These limitations will be addressed in a follow-up study of this cohort, in which lung function decline will be assessed in relation to baseline IOS measurements. Because of the potential for increased risk of the development of airways disease among firefighters, it is important to detect functional respiratory abnormalities as early as possible. From this study we conclude that application of IOS to the assessment of the respiratory health of metropolitan firefighters may result in the identification of peripheral airways dysfunction in significant numbers of individuals, even when spirometry values are within the normal range and in the absence of respiratory symptoms. In this study it was not possible to distinguish between airways dysfunction caused by cigarette smoking, occupational exposure or other exposures. IOS may also be helpful in distinguishing between firefighters

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Figure 2 Associations between quartiles of spirometry parameters and impulse oscillometry parameters in 488 firefighters. (a) Associations between FEV1 % predicted and mean DR5-R20 (left panel, *P = 0.038) and between FEV1 % predicted and mean AX (right panel, *P = 0.009). When smoking was included in the linear regression model (never, former and current smokers), P-values for the FEV1 % predicted quartile*smoking status interaction term were 0.213 for DR5-R20 and 0.079 for AX. (b) Associations between FEV1/FVC % predicted and mean DR5-R20 (left panel, *P = 0.024) and between FEV1/FVC % predicted and mean AX (right panel, *P = 0.015). When smoking was included in the linear regression model (never, former and current smokers), P-values for the FEV1/FVC % predicted quartile*smoking status interaction term were 0.108 for DR5-R20 and 0.081 for AX. (䊐) Never smokers. ( ) Current and former smokers. AX, integrated low frequency reactance area; R, respiratory resistance. *P-values are from FEV1 % predicted quartile* smoking status interaction term in the linear regression model, after controlling for age, BMI and exposure during firefighting and other professional activities (first quartile: 53.3%–90.1%; second quartile: 90.2%–98.1%; third quartile: 98.2%–106.1%; fourth quartile: 106.2%–128.3%). *P-values are from FEV1/FVC % predicted quartile* smoking status interaction term in the linear regression model, after controlling for age, BMI and exposure during firefighting and other professional activities (first quartile: 58.2%–91.7%; second quartile: 91.8%–97.1%; third quartile: 97.2%–101.6%; fourth quartile: 101.7%–116.8%). © 2010 The Authors Journal compilation © 2010 Asian Pacific Society of Respirology

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Figure 3 Association between FEV1/FVC % predicted quartiles and DR5-R20 when limited to asymptomatic firefighters (n = 391) (*P = 0.035). *P-value is from FEV1/FVC % predicted quartile*smoking status interaction term in the linear regression model, after controlling for age, BMI and exposure during firefighting and other professional activities. (䊐) Never smokers. ( ) Current and former smokers. R, respiratory resistance.

with abnormal spirometry tests due to airway/ parenchymal dysanapsis and those with acquired airways dysfunction.

ACKNOWLEDGEMENTS This study was funded by the SAMFS and The University of Adelaide. Acknowledgement is made of the generosity of the SAMFS firefighters who gave of their time and effort, and of the contribution to the study by the SAMFS staff. The authors very much appreciate the preparatory work for the study performed by Guillaume Dujardin, and the support in data collection, entry and processing by Daniel Blakeley.

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SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article: Appendix S1 Predicted normal equations for DR5R20 and AX from healthy non-smoking South Australian men. Please note: Wiley-Blackwell are not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.